An effective computational-screening strategy for simultaneously improving both catalytic activity and thermostability of α-l-rhamnosidase.

Catalytic efficiency and thermostability are the two most important characteristics of enzymes. However, it is always tough to improve both catalytic efficiency and thermostability of enzymes simultaneously. In the present study, a computational strategy with double-screening steps was proposed to simultaneously improve both catalysis efficiency and thermostability of enzymes; and a fungal α-l-rhamnosidase was used to validate the strategy. As the result, by molecular docking and sequence alignment analysis within the binding pocket, seven mutant candidates were predicted with better catalytic efficiency. By energy variety analysis, A355N, S356Y, and D525N among the seven mutant candidates were predicted with better thermostability. The expression and characterization results showed the mutant D525N had significant improvements in both enzyme activity and thermostability. Molecular dynamics simulations indicated that the mutations located within the 5 Å range of the catalytic domain, which could improve root mean squared deviation, electrostatic, Van der Waal interaction, and polar salvation values, and formed water bridge between the substrate and the enzyme. The study indicated that the computational strategy based on the binding energy, conservation degree and mutation energy analyses was effective to develop enzymes with better catalysis and thermostability, providing practical approach for developing industrial enzymes.